As traditional medical imaging equipment can only offer two-dimensional (hereinafter referred to as 2D) image of a human body inside, a doctor can just estimate the size and the shape of pathology from a plurality of 2D images so as to “conceive” the three-dimensional (hereinafter referred to as 3D) geometrical relationship between the pathology and its surrounding tissue, which brings difficulty in therapy. However, 3D visualization technology can reconstruct a 3D image from a series of 2D images and display it on a terminal. Therefore, not only a visual integral concept on the imaged object can be obtained, but also a lot of important 3D information can be saved. As the ultrasound imaging has significant advantages over CT, MRI in non-invasion, non-ionizing radiation and operation flexibility etc., the 3D ultrasound imaging certainly will be used widely in medical clinic. Therefore, it is indispensable to research 3D visualization technology of the ultrasound field.
Recently, there are two ways to acquire 3D ultrasound volume data: one is to acquire a series of 2D ultrasound images for tissues at known spatial positions using available 2D ultrasound diagnostic equipment in combination with a certain locating machine so as to obtain 3D volume data in an offline manner; the other is to acquire 3D volume data in real time using a 2D matrix array probe to emit volume ultrasound beams in pyramid shape.
“Rendering” means to obtain visual information using a visual algorithm to calculate volume data and finally display the visual information on a computer screen. Currently, 3D visual algorithms for ultrasound images are mainly divided into two classes. One is Surface Rendering, in which volume data should be classified to form intermediate geometrical elements, and then image rendering is achieved by means of traditional computer graphics technology. However, this algorithm is easily apt to produce a false plane display and cavity phenomenon for ultrasound images. The other is Direct Volume Rendering, in which 2D images displayed on the screen are generated directly from volume data. Using this algorithm, need for classifying the volume data and forming intermediate geometrical elements is eliminated, 3D medicinal information in detail is maintained and effects of integrative rendering are enhanced. However, this algorithm also increases the overhead for computation.
Ray casting algorithm is a method for direct volume rendering with principles as follows: virtual rays are emitted in the direction of connection between view point and each pixel on the view plane image and passes through and re-samples the volume data; and then gray-scale value of the pixel is finally obtained by synthesizing the re-sampled volume data according to an optical absorption-emission model, as shown in FIG. 1. This algorithm is a classical method for visualizing 3D volume data, but for a large amount of volume data of 3D ultrasound image, it is difficult to meet real-time demand because of high overhead for computation.
In fact, to be real-time is a vital factor that has restricted the practical application of 3D ultrasound for years. Therefore, researches on methods for fast rendering of 3D ultrasound images become essential and will be significant for medical clinic.
A method for accelerating 3D ultrasound imaging is disclosed in a patent document US 2002/0007680 A1, entitled “Method for Examining Objects using Ultrasound”. In this document, the method is described as follows: dividing volume data into several regions and determining whether or not more information is included in the part of the object corresponding to each region; applying higher scanning density and/or faster scanning frequency to the part of the object with more information, otherwise, applying sparser scanning density and/or slower scanning frequency. However, the method disclosed in the above-mentioned patent document is performed in the front end and it is difficult to manufacture such equipment. Besides, The method suffers from two main disadvantages. The first disadvantage lies in the rough division of the volume data, thereby it is hard to ensure the consistency of information density in each part of one region. The second disadvantage derives from inaccuracy of using “a moving detector” to determine whether or not the part of the object corresponding to each region has more information. Following are the reasons. Firstly, intensity of movement cannot completely represent density of information. Secondly, the result determined from current frame data will not guarantee to be effective in next frame. Thirdly, each time when the equipment is initiated and parameters are changed, rendering can be stable only after at least being adapted to the first two frames.